Crowdsourced earthquake early warning

Abstract

Earthquake early warning (EEW) can reduce harm to people and infrastructure from earthquakes and tsunamis, but it has not been implemented in most high earthquake-risk regions because of prohibitive cost. Common consumer devices such as smartphones contain low-cost versions of the sensors used in EEW. Although less accurate than scientific-grade instruments, these sensors are globally ubiquitous. Through controlled tests of consumer devices, simulation of an M w (moment magnitude) 7 earthquake on California's Hayward fault, and real data from the M w 9 Tohoku-oki earthquake, we demonstrate that EEW could be achieved via crowdsourcing.

Keywords: Crowd-Sourcing; Earthquake Early Warning; Earthquakes; Geodesy; Natural Disasters; Seismology; Tsunami.

Fig. 1. Global seismic hazard and extent of EEW.

Symbols show the few regions of the world where public citizens and organizations currently receive earthquake warnings and the types of data used to generate those warnings (7). Background color is peak ground acceleration with 10% probability of exceedance in 50 years from the Global Seismic Hazard Assessment Program.

Fig. 2. Device tests.

(A) Comparison of displacements obtained from consumer GNSS receivers with and without phase smoothing (p-s) and SBAS, by twice integrating smartphone acceleration and by Kalman filtering acceleration and GNSS data. Almost any smartphone or similar consumer device would generate the displacement and acceleration data shown with gray lines. Although these time series individually do a poor job of reproducing the true time history of motion (shown in black), they can be combined using a Kalman filter. This process produces one unified estimate of displacement (cyan) that is much less noisy than the original acceleration and displacement data used as inputs to the filter. However, the best GNSS hardware found in consumer devices (shown in red) is such high quality that there is no need to supplement the data with acceleration observations, and in fact, the displacement time series could be degraded by doing so. (B) Drift of position obtained from various devices (GNSS, double-integrated accelerometers, and Kalman filtering thereof) compared to observed earthquake displacements (7). Neither GNSS displacement observations nor acceleration data double integrated to displacement are stable over long periods. For GNSS data, this is because the inherent noise in the observations is not white noise. For acceleration data, this is because small tilts or steps in the observed acceleration cause large drifts when integrated. Thus, over time, the apparent position of sensors drifts, obscuring the true displacement of the instrument. The color curves show the apparent drift expected for each sensor and data type based on controlled tests. The black lines are observed displacement time series for earthquakes of different magnitudes. Thus, anywhere that a colored line is below a black line, the signal-to-noise ratio for that data type is greater than 1. In a crowdsourced setting, we expect to obtain data ranging in quality from a Kalman filter of C/A code data with acceleration data (cyan line created by combining data from light blue line with light green line), to C/A code data that have been phase-smoothed (“C/A code + p-s,” magenta line), to C/A code data that have been phase-smoothed and supplemented with SBAS (“C/A code + p-s + SBAS,” red line). Although all of these data types are significantly noisier than scientific-quality GNSS data (blue line), they are sensitive enough to record M6-7 earthquakes. (C) Using the drift curves shown in (B) and the peak ground displacement expected as function of magnitude and distance from the source (27), we can calculate the minimum magnitude earthquake observable with a signal-to-noise ratio of 10. Dotted line shows sensitivity of acceleration recorded on a smartphone. Dashed lines show sensitivity of displacement data obtained by twice integrating consumer and scientific acceleration data. At very close distances, the highest-quality consumer devices can observe earthquakes as small as M6 with a signal-to-noise ratio of at least 10.

Fig. 3. Hayward fault earthquake scenario.

(A) Representative displacement time series from Hayward fault rupture scenario. Black line: true displacement. Blue line: simulated smartphone C/A code GNSS. Green line: simulated smartphone accelerometer, twice integrated. Red line: Kalman filter combining GNSS and accelerometer. The red line is representative of the data we expect to observe with the least sophisticated consumer devices, yet it still does a good job of recovering the true ground motion shown in black. (B) Diamonds showing estimated epicentral location colored by time after origin. As soon as the earthquake is detected (at 5 s after origin), its epicenter can be estimated with an error of less than 5 km using consumer-quality data. Contour: S wave position when detection criterion is satisfied. Yellow text denotes major cities: SF, San Francisco; SJ, San Jose; OK, Oakland. Blue dots denote observer locations assuming 0.2% of the population within the blue box contribute data. (C) Number of observers who have detected a potential earthquake trigger as a function of time. The higher the density of observations, the sooner the detection criteria of a hundred triggers is reached. With just 0.2% of the population contributing data, the earthquake can be detected in 5 s. (D) Epicenter location error as a function of time. The error on the epicenter location is always <5 km even with very small percentages of the population contributing observations. (E) Estimated moment magnitude as a function of time for different participation levels. Black line: true magnitude. The estimated magnitude release almost perfectly reproduced the actual time history of moment release with very little latency, even for very low participation rates. The accuracy and low latency of the detection, location, and magnitude estimate of the earthquake based on very small numbers of consumer-quality observations suggest that a crowdsourced EEW system is feasible.

Fig. 4. Tohoku-oki earthquake example.

(A) Representative displacement time series observed for Tohoku-oki earthquake. Black line: scientific-grade GPS. Red line: consumer-grade (C/A code) GPS. C/A code GPS positions are the worst type of data we expect to obtain from consumer devices. However, even these data do a good job of recovering the actual displacement time series as shown by the scientific-grade GPS data. (B) Diamonds showing estimated epicentral location colored by time after origin. Waves indicate tsunami arrival times (28). Blue contour: S wave position when detection criterion is satisfied. Cyan contour: S wave position when S wave reaches Tokyo. Although there is higher latency in this example than the Hayward fault example due to the offshore location of the earthquake and the noisier data used, the proposed crowdsourcing approach could detect and locate the Tohoku earthquake before strong shaking reaches Tokyo and before the tsunami makes landfall. (C) Number of potential earthquake triggers versus time. We looked at the time series of C/A code positions before the earthquake to determine the frequency with which a trigger might be observed due to noise. We then expressed the number of triggers as SDs from that background triggering rate and then, to be very conservative, do not issue a warning until the number of observed triggers exceeds 5σ of the background triggering rate. (D) Red: location error of our estimated epicenter relative to the epicenter of (29). Purple: error associated with locations reported by Japan Meteorological Agency (JMA) EEW system. Brown: first location available from global monitoring (30). Although significantly slower than scientific-quality EEW (which includes offshore near-source observations from ocean-bottom seismometers), the consumer-quality data are capable of determining the earthquake’s location just as accurately as the scientific-quality JMA EEW system and do so significantly faster than an epicenter could be obtained from global scientific seismic data. (E) Red: estimated magnitude release as a function of time. Purple: M_j values reported by JMA’s EEW system. Brown: first M_w estimate available from global monitoring (30). Black: true magnitude from independent kinematic rupture model (24). Again, although there is more latency in the magnitude estimated using only onshore consumer-quality data than offshore scientific-quality data, the proposed crowdsourced EEW system is significantly faster than the global response to the earthquake. Also, note that the consumer-quality magnitude, which is based on GNSS data, does not saturate like the seismic magnitudes estimated from scientific-quality seismic data.